Phase 2 Progress Report
Tim Vigers
01 November 2019
Data
I started out by downloading all the advanced passing data from https://stats.nba.com/teams/passing/. Unfortunately this only goes back 6 years, so I am going to have to look at assist data as well. The NBA has been counting assists since the 1940s, but these data are much more subjective than counting passes made. Whether a pass leads to a basket is entirely up to the scorekeepers’ judgement, and there have been some famously questionable passes that were credited with an assist but probably should not have counted:
Passing Data Summary
| W (mean (SD)) | Passes.Made (mean (SD)) | Passes.Received (mean (SD)) | Ast (mean (SD)) | Secondary.Ast (mean (SD)) | Potential.Ast (mean (SD)) | Ast.Pts.Created (mean (SD)) | |
|---|---|---|---|---|---|---|---|
| Atlanta Hawks | 40.33 (13.06) | 25963.33 (828.32) | 25963.00 (828.98) | 2034.00 (88.84) | 263.00 (46.35) | 3998.33 (184.07) | 5059.67 (245.15) |
| Boston Celtics | 44.33 (10.54) | 25005.83 (981.29) | 25004.50 (981.30) | 1947.00 (167.38) | 246.33 (45.57) | 3896.17 (303.58) | 4834.83 (440.41) |
| Brooklyn Nets | 31.83 (10.46) | 24456.67 (1381.55) | 24456.17 (1381.91) | 1802.00 (110.69) | 216.50 (13.98) | 3713.83 (94.45) | 4537.67 (272.43) |
| Charlotte Bobcats | 42.00 (NA) | 26080.00 (NA) | 26080.00 (NA) | 1765.00 (NA) | 249.00 (NA) | 3696.00 (NA) | 4300.00 (NA) |
| Charlotte Hornets | 38.40 (5.77) | 23954.00 (614.01) | 23952.20 (615.09) | 1799.60 (102.48) | 258.80 (19.99) | 3638.00 (238.56) | 4548.40 (355.92) |
| Chicago Bulls | 38.33 (11.36) | 24836.17 (987.02) | 24835.17 (987.50) | 1843.83 (55.76) | 224.00 (51.56) | 3722.50 (313.23) | 4548.50 (159.57) |
| Cleveland Cavaliers | 43.83 (14.70) | 23392.50 (816.93) | 23347.67 (828.94) | 1811.50 (82.46) | 243.83 (48.50) | 3528.17 (188.56) | 4593.17 (305.88) |
| Dallas Mavericks | 38.33 (10.37) | 25387.33 (996.13) | 25386.67 (996.23) | 1841.00 (92.83) | 251.00 (29.43) | 3754.33 (187.15) | 4650.33 (185.40) |
| Denver Nuggets | 39.67 (8.91) | 23590.33 (1685.63) | 23544.83 (1623.96) | 1971.33 (174.08) | 237.67 (36.22) | 3856.33 (131.71) | 4890.67 (371.33) |
| Detroit Pistons | 37.00 (5.62) | 22949.50 (1586.45) | 22948.83 (1586.68) | 1754.00 (99.25) | 206.67 (28.30) | 3573.67 (88.08) | 4366.67 (233.98) |
| Golden State Warriors | 62.00 (8.44) | 25030.17 (2637.65) | 24979.00 (2612.71) | 2302.83 (217.14) | 320.83 (60.24) | 4215.83 (341.99) | 5657.83 (544.78) |
| Houston Rockets | 53.83 (7.65) | 22465.33 (1730.14) | 22464.67 (1730.19) | 1824.83 (123.50) | 210.67 (19.96) | 3718.50 (282.24) | 4679.83 (328.04) |
| Indiana Pacers | 46.17 (6.15) | 24636.50 (809.49) | 24635.50 (809.00) | 1819.00 (163.48) | 236.83 (37.58) | 3756.17 (174.23) | 4469.00 (346.21) |
| LA Clippers | 48.50 (4.80) | 24363.75 (579.51) | 24362.75 (579.55) | 1880.75 (61.85) | 232.25 (9.39) | 3469.50 (232.51) | 4645.75 (176.88) |
| Los Angeles Clippers | 56.50 (0.71) | 24539.50 (610.23) | 24537.50 (610.23) | 2023.50 (10.61) | 332.50 (3.54) | 3993.50 (53.03) | 5024.00 (36.77) |
| Los Angeles Lakers | 27.17 (7.76) | 23217.50 (1067.20) | 23215.67 (1067.21) | 1826.67 (230.63) | 202.17 (52.30) | 3658.17 (323.47) | 4481.17 (535.18) |
| Memphis Grizzlies | 40.83 (11.89) | 25676.17 (859.20) | 25675.17 (858.84) | 1790.00 (90.99) | 275.67 (29.46) | 3704.17 (123.62) | 4371.50 (236.01) |
| Miami Heat | 43.50 (6.53) | 24276.33 (577.63) | 24275.00 (577.92) | 1789.17 (135.82) | 240.00 (42.55) | 3713.17 (124.13) | 4486.17 (343.32) |
| Milwaukee Bucks | 39.00 (14.75) | 23836.33 (1058.14) | 23835.33 (1058.56) | 1924.00 (131.88) | 243.17 (16.87) | 3747.00 (163.20) | 4715.00 (388.58) |
| Minnesota Timberwolves | 33.17 (10.61) | 23528.50 (801.05) | 23527.50 (801.32) | 1907.83 (93.49) | 225.33 (21.48) | 3783.83 (305.62) | 4670.67 (270.69) |
| New Orleans Pelicans | 37.33 (7.31) | 23897.83 (1382.52) | 23896.17 (1381.58) | 1941.50 (208.37) | 189.83 (45.18) | 3700.67 (179.94) | 4773.17 (433.64) |
| New York Knicks | 27.00 (8.27) | 26131.33 (2455.30) | 26130.33 (2455.82) | 1725.83 (99.01) | 222.83 (18.38) | 3676.17 (264.09) | 4259.17 (173.54) |
| Oklahoma City Thunder | 50.33 (5.43) | 20943.67 (903.51) | 20942.17 (903.51) | 1781.67 (91.78) | 173.83 (14.19) | 3633.50 (144.54) | 4400.83 (221.17) |
| Orlando Magic | 29.50 (6.69) | 23672.67 (807.24) | 23671.83 (807.11) | 1853.00 (134.81) | 225.33 (28.70) | 3847.17 (182.96) | 4532.17 (342.47) |
| Philadelphia 76ers | 29.67 (17.85) | 26818.17 (1494.89) | 26817.17 (1493.63) | 1928.00 (220.61) | 224.33 (48.27) | 3976.83 (162.87) | 4828.33 (500.07) |
| Phoenix Suns | 28.83 (11.81) | 24133.50 (986.88) | 24131.83 (987.50) | 1699.00 (144.90) | 190.33 (27.17) | 3614.33 (153.85) | 4237.67 (277.96) |
| Portland Trail Blazers | 48.67 (5.16) | 23165.67 (690.48) | 23122.33 (703.77) | 1778.33 (112.40) | 227.50 (39.43) | 3650.50 (254.89) | 4462.33 (285.40) |
| Sacramento Kings | 31.33 (4.41) | 23959.00 (1732.21) | 23957.50 (1730.95) | 1812.50 (205.31) | 197.00 (44.14) | 3622.50 (282.87) | 4441.00 (515.66) |
| San Antonio Spurs | 56.67 (8.07) | 26086.83 (1758.87) | 26086.50 (1758.34) | 1982.33 (68.76) | 312.83 (37.49) | 3696.83 (250.39) | 4846.33 (196.89) |
| Toronto Raptors | 53.00 (5.02) | 23736.67 (1098.70) | 23736.33 (1098.86) | 1752.17 (242.45) | 238.67 (51.63) | 3465.17 (292.53) | 4403.67 (546.03) |
| Utah Jazz | 42.00 (9.90) | 27011.67 (1887.76) | 27011.33 (1888.15) | 1742.67 (205.47) | 232.33 (38.22) | 3710.17 (183.16) | 4424.67 (482.74) |
| Washington Wizards | 42.33 (6.19) | 23963.50 (1245.55) | 23963.00 (1245.80) | 2003.17 (84.49) | 249.67 (24.30) | 3920.17 (101.43) | 4929.17 (217.83) |
Basic Data Summary
| FG (mean (SD)) | FGA (mean (SD)) | FG% (mean (SD)) | 3P (mean (SD)) | 3PA (mean (SD)) | 3P% (mean (SD)) | AST (mean (SD)) | STL (mean (SD)) | BLK (mean (SD)) | TOV (mean (SD)) | PF (mean (SD)) | PTS (mean (SD)) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ATL | 3070.30 (508.37) | 6924.80 (825.48) | 0.44 (0.04) | 383.27 (277.66) | 1105.15 (751.77) | 0.32 (0.06) | 1791.01 (257.26) | 680.74 (106.48) | 411.63 (92.27) | 1313.61 (195.71) | 1903.14 (272.77) | 8103.49 (1151.76) |
| BOS | 3153.38 (608.61) | 7101.68 (1075.32) | 0.44 (0.05) | 399.20 (271.42) | 1137.72 (742.89) | 0.34 (0.04) | 1858.07 (363.49) | 663.37 (96.13) | 383.72 (86.66) | 1315.76 (199.10) | 1857.21 (230.01) | 8201.21 (1391.84) |
| CHA | 2995.52 (370.57) | 6636.59 (720.03) | 0.45 (0.02) | 441.14 (223.21) | 1243.03 (604.26) | 0.35 (0.03) | 1852.59 (260.47) | 628.17 (103.64) | 384.03 (69.48) | 1158.83 (132.75) | 1679.17 (224.48) | 7980.97 (908.58) |
| CHI | 3193.53 (359.70) | 6984.83 (674.12) | 0.46 (0.03) | 343.52 (224.55) | 979.55 (597.14) | 0.33 (0.06) | 1917.64 (224.34) | 646.98 (88.55) | 394.70 (67.34) | 1317.35 (223.50) | 1838.13 (235.89) | 8265.28 (845.09) |
| CLE | 3148.37 (371.90) | 6895.78 (743.35) | 0.46 (0.02) | 384.43 (274.12) | 1072.00 (724.17) | 0.34 (0.05) | 1869.02 (230.22) | 624.85 (71.69) | 397.54 (96.56) | 1253.98 (166.16) | 1802.61 (233.79) | 8156.14 (800.95) |
| DAL | 3146.67 (331.30) | 6847.77 (599.29) | 0.46 (0.02) | 438.54 (264.72) | 1240.97 (721.38) | 0.34 (0.04) | 1813.82 (240.33) | 603.08 (69.70) | 380.74 (63.30) | 1157.33 (155.53) | 1754.49 (220.89) | 8292.36 (829.51) |
| DEN | 3350.88 (445.17) | 7258.87 (816.21) | 0.46 (0.02) | 321.04 (257.35) | 953.02 (694.43) | 0.31 (0.05) | 1937.83 (321.36) | 700.07 (121.94) | 444.20 (86.30) | 1348.12 (215.97) | 1937.63 (230.95) | 8788.00 (1019.78) |
| DET | 3079.06 (554.17) | 6984.46 (931.05) | 0.44 (0.04) | 361.45 (248.32) | 1040.80 (676.36) | 0.32 (0.05) | 1727.14 (263.49) | 637.85 (109.30) | 398.85 (92.83) | 1275.00 (253.42) | 1875.13 (235.02) | 8052.79 (1203.94) |
| GSW | 3167.40 (620.72) | 7169.23 (956.50) | 0.44 (0.05) | 433.45 (307.14) | 1206.53 (762.44) | 0.33 (0.05) | 1812.63 (372.26) | 718.59 (110.57) | 414.87 (85.99) | 1346.61 (187.43) | 1865.42 (242.60) | 8247.95 (1458.68) |
| HOU | 3253.38 (386.19) | 7059.29 (807.72) | 0.46 (0.02) | 492.02 (340.98) | 1403.00 (927.60) | 0.33 (0.05) | 1902.98 (249.57) | 657.09 (87.32) | 417.80 (83.66) | 1339.15 (194.01) | 1824.08 (240.41) | 8506.37 (825.36) |
| IND | 3223.40 (393.49) | 6993.12 (775.67) | 0.46 (0.02) | 350.96 (220.67) | 1006.84 (577.89) | 0.33 (0.05) | 1861.29 (261.47) | 666.72 (120.84) | 409.09 (56.90) | 1336.31 (191.35) | 1904.77 (213.42) | 8458.69 (843.27) |
| LAC | 3208.82 (359.26) | 6934.73 (644.54) | 0.46 (0.02) | 352.70 (251.46) | 1028.85 (657.17) | 0.32 (0.05) | 1853.57 (254.84) | 660.80 (103.79) | 429.65 (76.23) | 1361.74 (223.67) | 1870.49 (206.59) | 8308.71 (788.21) |
| LAL | 3216.93 (533.64) | 7047.69 (793.17) | 0.45 (0.04) | 397.15 (246.51) | 1156.28 (682.82) | 0.32 (0.06) | 1894.76 (355.05) | 677.61 (101.86) | 447.33 (75.05) | 1296.63 (224.79) | 1810.45 (195.92) | 8390.32 (1179.95) |
| MEM | 2895.67 (316.54) | 6451.50 (641.86) | 0.45 (0.01) | 450.33 (163.52) | 1296.00 (455.67) | 0.35 (0.02) | 1693.38 (223.97) | 643.38 (80.76) | 399.17 (74.17) | 1200.62 (134.35) | 1706.79 (187.20) | 7689.17 (838.67) |
| MIA | 2967.87 (328.95) | 6470.29 (682.35) | 0.46 (0.02) | 480.10 (204.58) | 1350.90 (553.07) | 0.35 (0.02) | 1712.74 (196.02) | 614.26 (83.18) | 405.58 (62.19) | 1220.26 (178.49) | 1781.77 (245.45) | 7871.10 (818.69) |
| MIL | 3282.69 (394.62) | 6995.51 (680.19) | 0.47 (0.02) | 379.15 (231.44) | 1073.97 (626.92) | 0.34 (0.04) | 1951.35 (255.74) | 691.72 (113.10) | 395.41 (80.45) | 1299.78 (201.87) | 1894.73 (222.97) | 8428.78 (834.30) |
| MIN | 3031.57 (299.49) | 6672.40 (584.98) | 0.45 (0.01) | 372.40 (176.86) | 1089.30 (487.60) | 0.34 (0.03) | 1847.57 (214.75) | 610.83 (84.94) | 399.00 (72.78) | 1179.90 (162.74) | 1750.90 (214.04) | 7954.87 (786.83) |
| NJN | 3176.40 (392.90) | 7021.29 (677.42) | 0.45 (0.02) | 315.14 (264.01) | 938.63 (725.54) | 0.31 (0.05) | 1805.94 (246.07) | 692.00 (128.76) | 435.07 (106.66) | 1378.54 (244.99) | 1930.40 (266.14) | 8295.40 (824.54) |
| NOH | 3018.29 (295.93) | 6682.35 (514.43) | 0.45 (0.02) | 548.29 (169.20) | 1540.82 (471.74) | 0.36 (0.02) | 1762.00 (211.53) | 604.24 (58.66) | 384.47 (75.17) | 1125.94 (81.61) | 1657.82 (141.17) | 7977.35 (778.37) |
| NYK | 3049.22 (587.60) | 6870.47 (935.40) | 0.44 (0.04) | 402.20 (253.22) | 1146.12 (689.76) | 0.34 (0.04) | 1735.89 (354.50) | 645.24 (106.88) | 350.41 (75.19) | 1320.63 (196.82) | 1915.03 (264.70) | 7954.90 (1316.47) |
| OKC | 3277.12 (336.03) | 7065.94 (720.98) | 0.46 (0.02) | 404.45 (249.50) | 1144.38 (684.46) | 0.34 (0.05) | 1890.12 (229.24) | 716.70 (127.91) | 402.61 (75.30) | 1321.96 (188.37) | 1897.85 (231.22) | 8547.33 (746.25) |
| ORL | 3025.53 (342.34) | 6635.53 (675.45) | 0.46 (0.02) | 517.47 (220.96) | 1451.53 (590.67) | 0.35 (0.02) | 1751.17 (242.31) | 604.67 (71.86) | 384.23 (69.31) | 1220.67 (136.78) | 1715.90 (236.29) | 8054.97 (876.04) |
| PHI | 3124.44 (522.83) | 7012.01 (900.01) | 0.44 (0.04) | 327.20 (241.55) | 975.73 (681.18) | 0.32 (0.04) | 1796.84 (266.13) | 712.74 (86.02) | 444.24 (106.87) | 1374.85 (240.37) | 1866.73 (239.09) | 8253.84 (1171.70) |
| PHO | 3343.47 (335.39) | 7078.53 (628.76) | 0.47 (0.02) | 412.55 (267.35) | 1161.08 (704.60) | 0.33 (0.05) | 2019.41 (257.41) | 687.83 (120.12) | 395.65 (67.59) | 1358.43 (246.22) | 1864.35 (224.91) | 8687.27 (818.06) |
| POR | 3257.51 (384.89) | 6984.41 (701.97) | 0.47 (0.02) | 391.50 (258.22) | 1109.97 (684.08) | 0.33 (0.06) | 1914.29 (259.05) | 666.41 (118.46) | 403.39 (53.52) | 1323.61 (239.78) | 1849.53 (238.99) | 8471.57 (885.25) |
| SAC | 3157.23 (509.71) | 7019.83 (832.66) | 0.45 (0.03) | 373.48 (227.03) | 1053.67 (597.75) | 0.33 (0.05) | 1854.15 (272.21) | 672.85 (93.68) | 374.96 (65.05) | 1342.78 (197.03) | 1915.52 (252.62) | 8247.46 (1141.10) |
| SAS | 3306.92 (407.50) | 6968.52 (766.57) | 0.47 (0.02) | 321.49 (249.86) | 892.57 (628.14) | 0.33 (0.06) | 1915.98 (278.52) | 653.93 (110.40) | 463.37 (91.99) | 1306.88 (226.79) | 1813.58 (294.27) | 8592.27 (925.93) |
| TOR | 2958.46 (359.04) | 6568.75 (677.11) | 0.45 (0.02) | 550.17 (191.33) | 1524.00 (508.61) | 0.36 (0.02) | 1729.17 (227.96) | 606.25 (87.52) | 411.42 (91.87) | 1130.08 (143.40) | 1749.54 (178.95) | 7925.75 (984.27) |
| UTA | 3156.11 (344.55) | 6677.38 (670.47) | 0.47 (0.02) | 308.15 (233.35) | 880.75 (635.06) | 0.33 (0.04) | 1986.71 (247.32) | 670.29 (81.73) | 450.04 (101.36) | 1348.71 (204.85) | 1914.16 (215.83) | 8301.36 (771.99) |
| WAS | 3284.17 (356.45) | 7211.33 (768.70) | 0.46 (0.02) | 332.12 (242.25) | 972.02 (642.85) | 0.31 (0.06) | 1825.72 (237.61) | 647.72 (82.04) | 404.37 (84.29) | 1295.04 (182.54) | 1852.90 (181.92) | 8429.12 (810.86) |
Plots
First I looked at raw assist numbers by team and season:
I forgot about the lockout seasons in 1998 and 2011, so I’ll have to look at percentages or exclude those years. However, this is a good check that my data scraping function worked correctly. It does look like there could be an overall sinusoid trend though, which is interesting.
Next, I plotted the percentage of baskets assisted. This time I also split the plot into individual teams:
These plots look a little bit flatter to me, but it still looks like there’s an up and down trend. It would be worth checking for at least a cubic effect of time. Two random interesting things that stood out to me are the assist trends for the Utah Jazz and Golden State Warriors:
Golden State has a sudden increase in assist percentage around 2012, which is close to when they began their dominant run. The numbers peak in 2016, when Stephen Curry was the unanimous MVP. In Utah, the percentage of assisted baskets is consistently high from about 1985 to the early 2000s, which corresponds with the career of John Stockton (the NBA’s all-time leader in assists).
Because line graphs can be messy and hard to read, I plotted using Loess smoothing as well:
These are a little messy as well, but the cubic trend is pretty obvious in the league-wide plot.
Models
I’m starting out with a very basic model, to see if overall passing has has changed since 2013. This model has a random intercept for team, but I’d like to check whether or not I should include an AR(1) structure for the repeated measures as well. These models are adjusted for minutes played, since that likely has a large effect on total passes.
passing_mod <- lme(Passes.Made ~ poly(Season,3) + Min, random = ~1|Team,
data = passing)
passing_mod_ar1 <- lme(Passes.Made ~ poly(Season,3) + Min, random = ~1|Team,
data = passing,correlation = corAR1())The model with an AR(1) structure is better by AIC and the residuals look better as well. There still appears to be a pattern in the residuals for the model with random intercept only. It might be there in the AR(1) model residuals as well, but they look a little better.
| df | AIC | |
|---|---|---|
| passing_mod | 7 | 3097.662 |
| passing_mod_ar1 | 8 | 3059.728 |
I wanted to try a locally smoothed model as well
passing_loess <- loess(Passes.Made ~ Season + Min, data = passing, span = 0.28)